Different clinical features in Malawian outpatients presenting with COVID-19 prior to and during Omicron variant dominance: A prospective observational study

The SARS-CoV-2 Omicron variant has resulted in a high number of cases, but a relatively low incidence of severe disease and deaths, compared to the pre-Omicron variants. Therefore, we assessed the differences in symptom prevalence between Omicron and pre-Omicron infections in a sub-Saharan African population. We collected data from outpatients presenting at two primary healthcare facilities in Blantyre, Malawi, from November 2020 to March 2022. Eligible participants were aged >1month old, with signs suggestive of COVID-19, and those not suspected of COVID-19, from whom we collected nasopharyngeal swabs for SARS-CoV-2 PCR testing, and sequenced positive samples to identify infecting-variants. In addition, we calculated the risk of presenting with a given symptom in individuals testing SARS-CoV-2 PCR positive before and during the Omicron variant-dominated period. Among 5176 participants, 6.4% were under 5, and 77% were aged 18 to 50 years. SARS-CoV-2 infection prevalence peaked in January 2021 (Beta), July 2021 (Delta), and December 2021 (Omicron). We found that cough (risk ratio (RR), 1.50; 95% confidence interval (CI), 1.00 to 2.30), fatigue (RR 2.27; 95% CI, 1.29 to 3.86) and headache (RR 1.64; 95% CI, 1.15 to 2.34) were associated with a high risk of SARS-CoV-2 infection during the pre-Omicron period. In comparison, only headache (RR 1.41; 95% CI, 1.07 to 1.86) did associate with a high risk of SARS-CoV-2 infection during the Omicron-dominated period. In conclusion, clinical symptoms associated with Omicron infection differed from prior variants and were harder to identify clinically with current symptom guidelines. Our findings encourage regular review of case definitions and testing policies to ensure case ascertainment.


Introduction
As of June 2022, the COVID-19 pandemic has resulted in 543 million cases and 6.3 million deaths globally [1]. In sub-Saharan Africa, the pandemic has however been associated with a lower rate of hospitalisation and deaths than in Europe and the Americas [1], despite widespread SARS-CoV-2 community transmission [2], and low COVID-19 vaccine coverage [3]. Due to the continued emergence of SARS-CoV-2 variants of concern, surveillance is essential for monitoring the pandemic and informing public health interventions, however the optimal approach to surveillance in low-income, resource-poor settings is unclear [4].
By June 2022, Malawi had experienced four epidemic waves peaking in July 2020, January 2021, July 2021, and December 2021. There were 86,348 confirmed SARS-CoV-2 cases nationally, with 2,645 COVID-19-associated deaths [1]. However, due to the availability of testing there is considerable case under ascertainment, as evidence by the high seroprevalence of >65% observed in Malawi as of July 2021 [5]. Blantyre has had the highest number of reported COVID-19 cases in Malawi, with 28.6% of the national cases [6]. Recently, the Omicron variant has resulted in less hospitalisations and mortality in Malawi compared to the Delta variant [1], which has coincided with high seroprevalence of SARS-CoV-2 antibodies in Malawi and across sub-Saharan Africa [5,7]. Further, Malawi introduced COVID-19 vaccines in March 2021 [8]. The COVID-19 vaccination coverage for Malawi is 4.5%, including the AstraZeneca, Janssen and Pfizer vaccines, with AstraZeneca vaccine constituting most of the doses [8].
Using data from early in the pandemic, a standardised case definition for COVID-19 was developed by the World Health Organisation (WHO) [9] and United States Centre for Disease Control (US CDC) [10], and these have allowed targeted SARS-CoV-2 testing. However, in sub-Saharan Africa, there is a high burden of other febrile illnesses such as malaria, pneumonia, TB and salmonellosis that have clinical features that overlap with COVID-19 [11,12]. Further, there is limited data on the differences in clinical presentation between infections caused by different variants of concern (VOC), especially amongst non-hospitalised patients. To address these gaps, our study measured the prevalence of PCR-confirmed SARS-CoV-2 infection among outpatients presenting with medical conditions at primary healthcare facilities and compared the symptom profiles between Omicron and pre-Omicron infections.

Study design and population
From November 2020 to March 2022, we conducted a SARS-CoV-2 prevalence study in primary healthcare facilities in the city of Blantyre, southern Malawi. Blantyre is Malawi's commercial city with a population of 800,264 (pop density, 3334/km 2 ). Adults and children were recruited voluntarily from two government-owned primary healthcare facilities, Ndirande Health Centre and Limbe Health Centre, both overseen by the Blantyre District Health Office. Census data shows Ndirande HC serves a catchment area of 135,736, while Limbe HC serves a catchment area of 145,604, but the actual catchment is likely much higher.
From November 2020 to July 2021, individuals with medical conditions were screened at the facility's outpatient services department. Following assessment by a clinician and a review of the individual's health passport (patient retained medical record), a nasopharyngeal swab was collected for SARS-CoV-2 screening by SARS-CoV-2 by RT-PCR from patients suspected of COVID-19 according to the WHO case definition [9]. From August 2021, following a protocol amendment to include capturing a more detailed clinical history using the ISARIC symptom list [13][14][15], participants included both those suspected and those not suspected of COVID-19 according to the WHO case definition, with a 2:1 numerical bias towards those with suspected COVID-19. Patients with suspected COVID-19 were recruited as and when they presented to the facility, while those with not clinically suspected of COVID-19 were selected by approaching every third patient in health facility's triage area.

Data and specimen collection
Following informed consent and assent (for children) from August 2021, we used an abridged International Severe Acute Respiratory and Emerging Infection Consortium (ISARIC) Clinical Characterisation Protocol (CCP) electronic case report form (eCRF) [13][14][15] to collect demographic and clinical data from all participants. Study nurses collected nasopharyngeal swabs in Universal Transport Medium (UTM) (Copan, Brescia, Italy) from all participants. Samples were initially stored and transported to the Malawi-Liverpool-Wellcome Programme (MLW) laboratory on ice and processed within 48 hours.

Laboratory testing
Nasopharyngeal swabs were tested for SARS-CoV-2 RNA using the CDC 2019-nCoV RNA RT-PCR diagnostic panel (Integrated DNA Technologies, Iowa, USA). A cycle threshold (Ct) value of <40 was considered positive for SARS-CoV-2 using QuantStudio Real-Time PCR software v1.3 (Applied Biosystems, UK). Ribonuclease protein was used as an internal control to identify presence of human RNA. A negative extraction control and a PCR no-template control were also performed with every test. The results of patients with positive PCR tests were shared with the Blantyre District Health Office for further follow up and patient management.

Genomic sequencing and analysis
Samples were extracted using the Qiasymphony-DSP mini kit 200 (Qiagen, UK) with offboard lysis. Samples were then tested using the CDC N1 assay to confirm the Ct values before sequencing. Samples with a Ct value <27 were sequenced. The following sequencing protocols were used; ARTICv2 and v3 was used from November 2020 from July 2021 to July 2021 [16] and UNZA [17] from August 2021 on wards. Initially two primer pools were used, however a third pool was made for primer pairs that commonly had lower depth compared to the average [15]. PCR cycling conditions were adapted to the new sequencing primers, with annealing temperature changed to 60˚C. Sequencing was carried out with the Oxford Nanopore Technologies MinION sequencer. Samples that had poor coverage (<70%) with the ARTIC primer set were repeated with the UNZA primer set.

Statistical analysis
We performed statistical analyses and graphical presentation using R statistical package, version 4.1.0. Categorical variables were summarized using frequency distributions and compared using Pearson's Chi-squared test and Fisher's exact test. The continuous variables were presented as median with interquartile range.
We employed multivariable logistic regression models, as implemented in the R package stats (version 3.6.2), to investigate odds of presenting with particular symptoms in Omicron compared pre-Omicron phases, adjusting for age and sex. A multivariable logistic regression model adjusting for age, sex, vaccination status, vaccination doses and days since last vaccine dose was also employed to investigate the impact of COVID-19 vaccination on PCR-confirmed SARS-CoV-2. P-values <0.05 were considered significant.

Ethics approval
The study was approved by the College of Medicine Research and Ethics Committee (COM-REC P.08/20/3099) and Liverpool School of Tropical Medicine Research Ethics Committee (LSTMREC 21-058). Written informed consent was obtained from the parent/guardian of each participant under 18 years of age, and from individual adult participants.

Impact of COVID-19 vaccination on the risk of PCR-confirmed SARS-CoV-2 infection
Eighty percent (2009/2520) of the participants with detailed symptomology were eligible for vaccination (18 years and above) and had complete vaccination history. We, therefore, used these individuals to determine whether the risk of PCR-confirmed SARS-CoV-2 infection was Grey area represents confidence intervals. C) SARS-CoV-2 variants of concern across the three pandemic waves. D) SARS-CoV-2 variants of concern across age groups. (n = 402). https://doi.org/10.1371/journal.pgph.0001575.g001

Symptoms associated with PCR-confirmed SARS-CoV-2 infection change with variants
Based on the genomic surveillance data (Fig 1C), we assigned all individuals infected within the period from August 2021 to November 2021 as pre-Omicron infections (most commonly Delta infections), and those from December 2021 to March 2022 as Omicron infections. Twenty-two percent (447/2009) of vaccine-eligible patients who had provided the date since the last vaccine dose had PCR-confirmed SARS-CoV-2 infection ( Table 4). Thirty-five out of 228 patients were less than 18 years of age and had PCR-confirmed SARS-CoV-2 infection (S2 Table). As such subsequent analyses only focused on the adult population (�18 years  Clinical symptoms associated with PCR-confirmed SARS-CoV-2 infections were different during the Omicron and pre-Omicron phases (S3 Table). Cough (70% vs. 54%, p<0.001), fatigue (14% vs 3.6%, p<0.001), loss of taste (10% vs. 5.0%, p = 0.033), headache (50% vs. 39%, p = 0.034), and abdominal pain (14% vs. 8.3%, p = 0.043) were more frequent in patients from the pre-Omicron phase compared to the Omicron phase. In contrast, muscle ache was more common in the Omicron phase than pre-Omicron phase (35% vs 25%, p = 0.029).   (Fig 2 and Table 5). Conversely, fever was independently associated with Omicron than pre-Omicron SARS-CoV-2 PCR positive diagnosis (OR 2.46 [CI 1.29-4.97]) (Fig 2 and Table 5).

Discussion
In a setting where there is a high burden of presentations with infectious disease, we found that the symptoms associated with SARS-CoV-2 Omicron infection have become considerably less distinct, differing significantly from those infections with pre-Omicron variants (predominantly Delta). Indeed fever, which is common to many infectious presentations [11,12], was most prevalent among presumed Omicron infected patients, while headache, cough, fatigue, and abdominal pain were significantly more prevalent among pre-Omicron cases.
Our data showing a different symptom profile associated with Omicron infection is consistent with studies elsewhere [21] with headache being prominent in three other studies from the UK [21][22][23]. However, we observed high odds for presenting with fever in presumed Omicron-infected patients than pre-Omicron patients, in contrast with the two studies in the UK [21,23]. The main differences between the Malawi study and the UK studies are age and prevalence of Omicron sub-lineages, with Malawi cohort being a younger population and having predominantly BA.1 at time of sampling. BA.1 is associated with a different symptom profile than BA.2 [22]. Together, our findings and those of others suggest that the clinical case definition of COVID-19 used for testing and surveillance may need to be revised to maintain case ascertainment.  Data from Malawi and elsewhere has shown that the Omicron variant has presented with less severe disease, hospitalisation and deaths, than the pre-Omicron VOCs [1,24]. The Omicron variant have been shown to be less capable of transition from the upper to lower respiratory tract infection [25], and this could potentially contribute to the low incidence of severe disease. However, data in non-immunised populations in Hong Kong indicate that Omicron is not intrinsically mild [26,27], suggesting that immune response or past exposure could be an important determinant of this low severity. Data from South Africa has shown that high SARS-CoV-2 seroprevalence has been associated with low number of deaths and hospitalisation attributed to the Omicron variant [24]. In Malawi, seroprevalence data has shown that more than 70% of the population had anti-SARS-CoV-2 receptor binding domain (RBD) antibodies before the Omicron variant pandemic wave [5]. In line with previous findings [28], COVID-19 vaccination was not associated with a reduced risk of PCR-confirmed SARS-CoV-2 infection, especially during the Omicron wave. It is therefore plausible that the altered clinical presentation observed in our study could also be attributed to pre-existing immunity from prior SARS-CoV-2 exposure.
Furthermore, our findings align with the temporal dynamics of the COVID-19 pandemic waves in Malawi and the region [1, 5,24]. A high SARS-CoV-2 prevalence of 30-50% among patients presenting to primary healthcare at the peak of the three pandemic waves, is consistent with high reported national COVID-19 cases during the same period [1]. Furthermore, consistent with genomic surveillance [15,29], our sentinel surveillance correctly identified the VOCs driving the local pandemic waves. Due to the consistency in our sampling over time, we were able to provide real-time data to aid public health response in Malawi, especially on the identification of VOCs driving community transmission. Collectively, this indicates that sentinel surveillance backed up by diagnostics and genomics data could be an early warning system for national pandemic response in resource-limited settings, considering that by the time hospitalisations are rising it is already too late to intervene effectively.
Our study had several limitations. Firstly, the study was conducted in urban Blantyre and findings may not be generalisable to rural settings. Secondly, our study cohort (median age 28 years (IQR 21-38)) was not fully representative of the population structure in Malawi (median age 17.5 years) [30]. Thirdly, since our genomic surveillance was limited to a subset of samples with low PCR CT values, this approach biases our identification of variants to those causing high viral burden infections at time of recruitment. Lastly, our analysis of the impact of COVID-19 vaccination did not adjust for immunity induced following previous exposure to SARS-CoV-2, as it has been previously shown to be protective against symptomatic COVID- 19 [31,32]. The SARS-CoV-2 seroprevalence has been reported to be very high in Malawi [5], despite a low vaccination coverage [33].
In conclusion, our study demonstrates changes in clinical symptoms overtime, aligned to infecting variant, indicating that case definitions of COVID-19 need constant monitoring and revision to match SARS-CoV-2 evolution to maintain its relevance for institutional and national testing policies. This study also highlights the importance and utility of sentinel surveillance in low-resourced settings to aid timely public health response against the COVID-19 pandemic and future pandemics.
Supporting information S1 Table. Clinical symptom presentation during the pre-Omicron and Omicron period.